What can a close reading of a Computer Science Learning Standards document tell us about the coherence and incoherence of these standards?
We used text mining tools developed for the R programming language, and especially those in the tidytext universe were used (Silge & Robinson, 2016, 2017).
In these word frequencies, we see what we would expect to see. In the executive summary, for example, we can see a focus on careers and career readiness in the frequency of words like occupation, jobs, and labor.
We see the same pattern in the concept areas comprising the learning standards. For example, when we look at the concept areas, such as cybersecurity and computational thinking, we see expected terms such as safeguards, secure, and protected and program, algorithm, and process.
Additionally, we found that the language of the learning standards is most frequently associated with the lower levels thinking skills, such as identify, explain, discuss, as developed by Bloom in his taxonomy.
These bigram network analyses further demonstrate the richness of the language in the executive summary and the narrowness in the languge of the learning standards.
Coherence - We found across these two sections in the areas of scope and concept and sub- concept areas.
Incoherence - We found an incoherence between the vision, guiding principles, and lenses in the executive summary, which focused on big ideas such as computer science for all, inclusivity, and interdisciplinary curriculum design, and the language of the learning standards themselves. Language pertaining to these big ideas were completely absent in the expression of the learning standards.
This incoherence is particularly important in the training and development of computer science teachers, as if the vision and guiding principles that are foundational to these learning standards are to be enacted in their fullness, it will be up to those teachers to do rich work in their design of curricular plans, activities, and materials.